Salīdzināt metodes

	Beijes kriginga (modelēta ģeostatistika)	Parastā krigēšana
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1993–1998	1963
Autors	Diggle, Tawn & Moyeed; Handcock & Stein	Georges Matheron (formalising D.G. Krige's empirical work)
Tips	Bayesian spatial interpolation	Geostatistical interpolation
Pirmavots	Diggle, P. J., Tawn, J. A., & Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(3), 299–350.	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266.
Citi nosaukumi	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging	OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor
Saistītās	5	4
Kopsavilkums	Bayesian Kriging embeds classical geostatistical interpolation inside a full probabilistic framework. Instead of treating variogram parameters as fixed point estimates, it places prior distributions on them and updates these priors with observed spatial data to obtain a posterior distribution. Predictions at unsampled locations are then marginalised over this uncertainty, yielding honest predictive intervals that account for both spatial dependence and parameter uncertainty.	Ordinary Kriging (OK) is the standard geostatistical method for interpolating a continuous spatial variable at unsampled locations. It derives optimal, unbiased weights from the spatial covariance structure of the data, making it the Best Linear Unbiased Predictor (BLUP) under stationarity assumptions. Unlike simpler distance-based methods, it also provides a prediction uncertainty (kriging variance) at every interpolated point.